This short video explains why companies use Hazelcast for business-critical applications based on ultra-fast in-memory and/or stream processing technologies.
Stream processing is a hot topic right now, especially for any organization looking to provide insights faster. But what does it mean for users of Java applications, microservices, and in-memory computing? In this webinar, we will cover the evolution of stream processing and in-memory related to big data technologies and why it is the logical next step for in-memory processing projects.
Now, deploying Hazelcast-powered applications in a cloud-native way becomes even easier with the introduction of Hazelcast Cloud Enterprise, a fully-managed service built on the Enterprise edition of Hazelcast IMDG. Can't attend the live times? You should still register! We'll be sending out the recording after the webinar to all registrants.
Overview
Member Rolling Upgrade
Upgrade cluster members without service interruption in embedded mode (no clients) or client-server mode (clients are connected to a cluster).
Client Rolling Upgrade
Upgrade clients individually with no interruptions to other clients, even when working off a central cache.
Supports mixing clients of different languages and versions in one cluster.
Architecture
Upgrade your cluster members without any service interruption and with complete migration of processing and data as each member is upgraded.
Upgrade the clients independent of the server, without service interruption, using Hazelcast Open Client Protocol,which supports six client languages including Java, C++, .NET, Python, Node.js, Go, and Scala.
Resources
Hazelcast IMDG is a clustered, in-memory data-grid that manages application data and distributes processing using in-memory storage and parallel execution for breakthrough application speed and scale. In this Quick Start Guide, learn what an in-memory data grid can be used for, how to do simple query operations with Hazelcast IMDG, what sharing means with Hazelcast, and more. This guide is intended for software architects and developers who are planning or building systems requiring distribute infrastructure for application scalability and performance.
Enrichment is a frequent technical use case in stream processing. It is a translation of the traditional star schema into the low-latency continuous processing world: the stream of facts is enriched using slowly changing dimension data. In this webinar you will learn how to do high-performance stream enrichment. We’ll discuss multiple ways of enrichment, explaining the trade-offs. We will feature hands-on examples and live coding using Hazelcast Jet 0.7.
Whether you're interested in learning the basics of in-memory systems, or you're looking for advanced, real-world production examples and best practices, we've got you covered.
